Accurate fish detection is of great significance in aquaculture. However, the non-uniform strong reflection in aquaculture ponds will affect the precision of fish detection. This paper combines YOLOv4 and CVAE to accurately detect fishes in the image with non-uniform strong reflection, in which the reflection in the image is removed at first and then the reflection-removed image is provided for fish detecting. Firstly, the improved YOLOv4 is applied to detect and mask the strong reflective region, to locate and label the reflective region for the subsequent reflection removal. Then, CVAE is combined with the improved YOLOv4 for inferring the priori distribution of the Reflection region and restoring the Reflection region by the distribution so that the reflection can be removed. For further improving the quality of the reflection-removed images, the adversarial learning is appended to CVAE. Finally, YOLOV4 is used to detect fishes in the high quality image. In addition, a new image dataset of pond cultured takifugu rubripes is constructed,, which includes 1000 images with fishes annotated manually, also a synthetic dataset including 2000 images with strong reflection is created and merged with the generated dataset for training and verifying the robustness of the proposed method. Comprehensive experiments are performed to compare the proposed method with the state-of-the-art fish detecting methods without reflection removal on the generated dataset. The results show that the fish detecting precision and recall of the proposed method are improved by 2.7% and 2.4% respectively.

In this letter, an effective low bit-rate image restoration method is proposed, in which image denoising and subspace regression learning are combined. The proposed framework has two parts: image main structure estimation by classical NLM denoising and texture component prediction by subspace joint regression learning. The local regression function are learned from denoised patch to original patch in each subspace, where the corresponding compression image patches are employed to generate anchoring points by the dictionary learning approach. Moreover, we extent Extreme Support Vector Regression (ESVR) as multi-variable nonlinear regression to get more robustness results. Experimental results demonstrate the proposed method achieves favorable performance compared with other leading methods.

Existing image deblurring methods with a blurred/noisy image pair take a two-step approach: blur kernel estimation and image restoration. They can achieve better and much more stable blur kernel estimation than single image deblurring methods. On the other hand, in the image restoration step, they do not exploit the information on the noisy image, or they require ad hoc tuning of interdependent parameters. This paper focuses on the image restoration step and proposes a new restoration method of using a blurred/noisy image pair. In our method, the image restoration problem is formulated as a constrained convex optimization problem, where data-fidelity to a blurred image and that to a noisy image is properly taken into account as multiple hard constraints. This offers (i) high quality restoration when the blurred image also contains noise; (ii) robustness to the estimation error of the blur kernel; and (iii) easy parameter setting. We also provide an efficient algorithm for solving our optimization problem based on the so-called alternating direction method of multipliers (ADMM). Experimental results support our claims.

This paper proposes a novel framework for enhancing underwater images captured by optical imaging model and non-local means denoising. The proposed approach adjusts the color balance using biasness correction and the average luminance. Scene visibility is then enhanced based on an underwater optical imaging model. The increase in noise in the enhanced images is alleviated by non-local means (NLM) denoising. The final enhanced images are characterized by improved visibility while retaining color fidelity and reducing noise. The proposed method does not require specialized hardware nor prior knowledge of the underwater environment.

In this letter, a local characteristic image restoration based on convolutional neural network is proposed. In this method, image restoration is considered as a classification problem and images are divided into several sub-blocks. The convolutional neural network is used to extract and classify the local characteristics of image sub-blocks, and the different forms of the regularization constraints are adopted for the different local characteristics. Experiments show that the image restoration results by the regularization method based on local characteristics are superior to those by the traditional regularization methods and this method also has lower computing cost.

Recovery of low-rank matrices has seen significant activity in many areas of science and engineering, motivated by theoretical results for exact reconstruction guarantees and interesting practical applications. Recently, numerous methods incorporated the nuclear norm to pursue the convexity of the optimization. However, this greatly restricts its capability and flexibility in dealing with many practical problems, where the singular values have clear physical meanings. This paper studies a generalized non-convex low-rank approximation, where the singular values are in lp-heuristic. Then specific results are derived for image restoration, including denoising and deblurring. Extensive experimental results on natural images demonstrate the improvement of the proposed method over the recent image restoration methods.

Considering the real existent fact of the ideal edge and the learning style of image analogy without reference parameters, a blind image recovery algorithm using a self-adaptive learning method is proposed in this paper. We show that a specific local image patch with degradation characteristic can be utilized for restoring the whole image. In the training process, a clear counterpart of the local image patch is constructed based on the ideal edge assumption so that identification of the Point Spread Function is no longer needed. Experiments demonstrate the effectiveness of the proposed method on remote sensing images.

Images captured under foggy conditions often exhibit poor contrast and color. This is primarily due to the air-light which degrades image quality exponentially with fog depth between the scene and the camera. In this paper, we restore fog-degraded images by first estimating depth using the physical model characterizing the RGB channels in a single monocular image. The fog effects are then removed by subtracting the estimated irradiance, which is empirically related to the scene depth information obtained, from the total irradiance received by the sensor. Effective restoration of color and contrast of images taken under foggy conditions are demonstrated. In the experiments, we validate the effectiveness of our method compared with conventional method.

The application of information hiding to image compression is investigated to improve compression efficiency for JPEG color images. In the proposed method, entropy-coded DCT coefficients of chrominance components are embedded into DCT coefficients of the luminance component. To recover an image in the face of the degradation caused by compression and embedding, an image restoration method is also applied. Experiments show that the use of both information hiding and image restoration is most effective to improve compression efficiency.

This letter proposes a new image colorization algorithm based on the sparse optimization. Introducing some assumptions, a problem of recovering a color image from a grayscale image with the small number of known color pixels is formulated as a mixed l0/l1 norm minimization, and an iterative reweighted least squares (IRLS) algorithm is proposed. Numerical examples show that the proposed algorithm colorizes the grayscale image efficiently.

This paper presents a restoration method using several degraded observed images obtained through a technique known as microscanning. It is shown that microscanning provides sufficient spatial information for image restoration with minimal information about the original image and without knowing the interference function that causes degradation.

This paper addresses problems appearing in restoration algorithms based on utilizing both Tikhonov and bilateral total variation (BTV) regularization. The former regularization assumes that prior information has Gaussian distribution which indeed fails at edges, while the later regularization highly depends on the selected bilateral filter's parameters. To overcome these problems, we propose a locally adaptive regularization. In the proposed algorithm, we use general directional regularization functions with adaptive weights. The adaptive weights are estimated from local patches based on the property of the partially restored image. Unlike Tikhonov regularization, it can avoid smoothness across edges by using adaptive weights. In addition, unlike BTV regularization, the proposed regularization function doesn't depend on parameters' selection. The convexity conditions as well as the convergence conditions are derived for the proposed algorithm.

In this study, we introduce shift-invariant sparse image representations using tree-structured dictionaries. Sparse coding is a generative signal model that approximates signals by the linear combinations of atoms in a dictionary. Since a sparsity penalty is introduced during signal approximation and dictionary learning, the dictionary represents the primal structures of the signals. Under the shift-invariance constraint, the dictionary comprises translated structuring elements (SEs). The computational cost and number of atoms in the dictionary increase along with the increasing number of SEs. In this paper, we propose an algorithm for shift-invariant sparse image representation, in which SEs are learnt with a tree-structured approach. By using a tree-structured dictionary, we can reduce the computational cost of the image decomposition to the logarithmic order of the number of SEs. We also present the results of our experiments on the SE learning and the use of our algorithm in image recovery applications.

A blur restoration scheme for images with linear motion blurred objects in still background is proposed. The proposed scheme starts from a rough detection of locations of blurred objects. This rough segmentation of the blurred regions is based on an analysis of local orientation map. Then, parameters of blur are identified based on a linear constant-velocity motion blur model for every detected blurred region. After the blur parameters are estimated, the locations of blurred objects can be refined before going to a restoration process by using information from the identified blur parameters. Blur locations are refined by observing local power of the blurred image which is filtered by a high-pass filter. The high-pass filter has approximately a frequency characteristic that is complementary to the identified blur point spread function. As a final step, the image is restored by using the estimated blur parameters and locations based on an iterative deconvolution scheme applied with a regularization concept. Experimental examples of simulated and real world blurred images are demonstrated to confirm the performance of the proposed scheme.

In this paper, an ER (Error-Reduction) algorithm-based method for removal of adherent water drops from images obtained by a rear view camera mounted on a vehicle in rainy conditions is proposed. Since Fourier-domain and object-domain constraints are needed for any ER algorithm-based method, the proposed method introduces the following two novel constraints for the removal of adherent water drops. The first one is the Fourier-domain constraint that utilizes the Fourier transform magnitude of the previous frame in the obtained images as that of the target frame. Noting that images obtained by the rear view camera have the unique characteristics of objects moving like ripples because the rear view camera is generally composed of a fish-eye lens for a wide view angle, the proposed method assumes that the Fourier transform magnitudes of the target frame and the previous frame are the same in the polar coordinate system. The second constraint is the object-domain constraint that utilizes intensities in an area of the target frame to which water drops have adhered. Specifically, the proposed method models a deterioration process of intensities that are corrupted by the water drop adhering to the rear view camera lens. By utilizing these novel constraints, the proposed ER algorithm can remove adherent water drops from images obtained by the rear view camera. Experimental results that verify the performance of the proposed method are represented.

In this paper, a method for adaptive reconstruction of missing textures based on kernel canonical correlation analysis (CCA) with a new clustering scheme is presented. The proposed method estimates the correlation between two areas, which respectively correspond to a missing area and its neighboring area, from known parts within the target image and realizes reconstruction of the missing texture. In order to obtain this correlation, the kernel CCA is applied to each cluster containing the same kind of textures, and the optimal result is selected for the target missing area. Specifically, a new approach monitoring errors caused in the above kernel CCA-based reconstruction process enables selection of the optimal result. This approach provides a solution to the problem in traditional methods of not being able to perform adaptive reconstruction of the target textures due to missing intensities. Consequently, all of the missing textures are successfully estimated by the optimal cluster's correlation, which provides accurate reconstruction of the same kinds of textures. In addition, the proposed method can obtain the correlation more accurately than our previous works, and more successful reconstruction performance can be expected. Experimental results show impressive improvement of the proposed reconstruction technique over previously reported reconstruction techniques.

Image restoration based on Bayesian estimation in most previous studies has assumed that the noise accumulated in an image was independent for each pixel. However, when we take optical effects into account, it is reasonable to expect spatial correlation in the superimposed noise. In this paper, we discuss the restoration of images distorted by noise which is spatially correlated with translational symmetry in the realm of probabilistic processing. First, we assume that the original image can be produced by a Gaussian model based on only a nearest-neighbor effect and that the noise superimposed at each pixel is produced by a Gaussian model having spatial correlation characterized by translational symmetry. With this model, we can use Fourier transformation to calculate system characteristics such as the restoration error and also minimize the restoration error when the hyperparameters of the probabilistic model used in the restoration process coincides with those used in the formation process. We also discuss the characteristics of image restoration distorted by spatially correlated noise using a natural image. In addition, we estimate the hyperparameters using the maximum marginal likelihood and restore an image distorted by spatially correlated noise to evaluate this method of image restoration.

This paper shows that there is a fruitful world behind sampling theorems. For this purpose, the sampling problem is reformulated from a functional analytic standpoint, and is consequently revealed that the sampling problem is a kind of inverse problem. The sampling problem covers, for example, signal and image restoration including super resolution, image reconstruction from projections such as CT scanners in hospitals, and supervised learning such as learning in artificial neural networks. An optimal reconstruction operator is also given, providing the best approximation to an individual original signal without our knowing the original signal.

In this paper, a Kalman filter-based method for restoration of video images acquired by an in-vehicle camera in foggy conditions is proposed. In order to realize Kalman filter-based restoration, the proposed method clips local blocks from the target frame by using a sliding window and regards the intensities in each block as elements of the state variable of the Kalman filter. Furthermore, the proposed method designs the following two models for restoration of foggy images. The first one is an observation model, which represents a fog deterioration model. The proposed method automatically determines all parameters of the fog deterioration model from only the foggy images to design the observation model. The second one is a non-linear state transition model, which represents the target frame in the original video image from its previous frame based on motion vectors. By utilizing the observation and state transition models, the correlation between successive frames can be effectively utilized for restoration, and accurate restoration of images obtained in foggy conditions can be achieved. Experimental results show that the proposed method has better performance than that of the traditional method based on the fog deterioration model.

Considering the inaccuracy of image registration, we propose a new regularization restoration algorithm to solve the ill-posed super-resolution (SR) problem. Registration error is used to obtain cross-channel error information caused by inaccurate image registration. The registration error is considered as the noise mean added into the within-channel observation noise which is known as Additive White Gaussian Noise (AWGN). Based on this consideration, two constraints are regulated pixel by pixel within the framework of Miller's regularization. Regularization parameters connect the two constraints to construct a cost function. The regularization parameters are estimated adaptively in each pixel in terms of the registration error and in each observation channel in terms of the AWGN. In the iterative implementation of the proposed algorithm, sub-sampling operation and sampling aliasing in the detector model are dealt with respectively to make the restored HR image approach the original one further. The transpose of the sub-sampling operation is implemented by nearest interpolation. Simulations show that the proposed regularization algorithm can restore HR images with much sharper edges and greater SNR improvement.