In order to solve the problems about color distortion and low contrast of underwater images, we propose an underwater image enhancement algorithm that combines adaptive color correction with improved Retinex algorithm. Our algorithm is a single-image enhancement method that does not require specialized hardware and underwater scenes prior. Firstly, the adaptive color correction is carried out on the underwater distorted images to solve the color cast problem effectively. Then, on the one hand, we use the image decomposition to strengthen the detail part and obtain a detail enhanced image. On the other hand, we use the improved Retinex algorithm to strengthen the edge part and obtain an edge enhanced image. Finally, the detail enhanced image and the edge enhanced image are fused based on the non-subsampled shearlet transform (NSST) to obtain the final enhanced underwater image. The results show that our method outperforms several state-of-the-art methods about underwater image enhancement in terms of PCQI, UCIQE, UIQM and IE. By scale invariant feature transform (SIFT) algorithm, we calculate the number of feature matching points of the input image and the enhanced image, and our proposed method achieves the best experimental results. The source code of our proposed algorithm is available at: https://github.com/lin9393/ underwater-image-enhance.

Traditional image denoising methods, which do not depend on data training, have good interpretability. However, traditional image denoising methods hardly achieve the denoising effect of deep learning methods. Based on traditional image processing techniques, this paper proposes a new hybrid image denoising model. The block-batching and 3-D filtering (BM3D) algorithm is used to obtain the first denoised image. The weighted kernel norm minimization (WNNM) and non-subsampled shearlet transform (NSST) algorithms are successively adopted to get the second denoised image. By the gradient domain guided filtering, the texture information of the first denoised image is extracted to enhance the details of the second denoised image. Specially, we propose the adaptive iterative NSST algorithm based on the improved soft thresholding, in order to solve the problems about the discontinuity of the hard thresholding and the constant deviation of the soft thresholding. Our approach can not only attenuate excessive smoothing, but also restore the natural appearance of the image. Experiments are conducted to demonstrate that our proposed method enjoys PSNR and SSIM performance gains over several deep learning denoising methods.

The goal of blind text image deblurring is to obtain a clean text image from the given blurry text image without knowing the blur kernel. Sparsity-based methods have been shown their effectiveness in various blind text image deblurring models. However, the blur kernel estimation methods based on sparse priors lack of the consideration for the brightness information about the blur kernel, which will affect the restoration effect of the blur kernel. Besides, previous methods seldom apply sparse priors to both spatial domain and transform domain information. We propose a novel blind text image deblurring model based on multi-scale fusion and sparse priors. Besides the sparse gradient prior on the latent clean text image, we add the sparse prior on the high-frequency wavelet coefficients of the latent text image, which will better constrain the solution space and obtain good clean images. The semi-quadratic splitting method is used to alternately optimize the blur kernel and the latent clean image. Meanwhile, we consider the influence of the brightness feature of the restored blur kernel. By multi-scale fusion technique on the basis of Laplacian weight and saliency weight, we fuse the computed blur kernels in three channels to improve the quality of blur kernel. The experimental results show that our algorithm has good results in the restoration of blur kernels and text images.