For undersea activities, underwater vehicles usually carry optical imaging systems for recording. The captured images and videos frequently suffer from two displeasing problems: First, color distortion; and second, poor visibility. This is mainly because that the light is exponentially attenuated while penetrating through water and the strength of attenuation is color dependent. This paper develops an effective single underwater image restoration framework based on the depth estimation and the transmission compensation. To address the consequences of scattering and absorption, our restoration scheme consists of five major phases: 1) background light estimation; 2) submerged dark channel prior; 3) transmission refinement and radiance recovery; 4) point spread function deconvolution; and 5) transmission and color compensation. A wide variety of underwater images with various scenarios were exploited to assess the restoration performance of the proposed algorithm. Various evaluation metrics were employed to analyze the experimental results. It was suggested that this new restoration algorithm outperformed many state-of-the-art methods both qualitatively and quantitatively. In addition, potential applications regarding autopilot and three-dimensional visualization were demonstrated. We believe that our underwater image restoration technique is promising in many undersea activities that require high-quality images.

In feature-based remote sensing image registration, the scale-invariant feature transform (SIFT) algorithm has been one of the most popular solutions. However, it is still a challenge to possess an appropriate amount of correct matches while eliminating mismatches. In this letter, inspired by SIFT, an accurate and robust feature matching framework based on feature slope grouping (FSG) for remote sensing image registration is proposed. Our FSG-SIFT algorithm consists of four major phases: modified SIFT, feature slope computation, feature point grouping, and outlier removal and transformation. Specifically, the random sample consensus is adopted to refine the matches followed by the affine transform. The proposed remote sensing image registration algorithm has been validated on a wide variety of high-resolution orthoimagery data. Experimental results with multispectral and multitemporal images suggested that this new image registration algorithm well improved the feature matching accuracy with better registration performance over five state-of-the-art methods.

Underwater images are often deteriorated with blurring, darkness, poor visual quality of low contrast, and color diminishing. This is mainly due to the fact that the light is exponentially attenuated while traveling through water and the strength of attenuation is color dependent. After constructing a simplified image formation model, this paper proposes a new strategy for single underwater image restoration. In light of different perspectives, two distinct transmission coefficient estimation approaches have been developed. One is based on the optical characteristics while the other relies on the essence of image processing knowledge. Subsequently, these two transmission maps are fused to produce the final outcome, which is adaptively weighted by their respective saliency maps. The obtained signal radiance is dissolved through point spread function deconvolution and color compensation to produce the final scene radiance. A variety of underwater images with various scenarios were exploited to evaluate the restoration performance. Experimental results demonstrated the superiority of the proposed algorithm over other competitive methods for underwater image restoration.

Noise estimation is fundamental and essential in a wide variety of computer vision, image, and video processing applications. It provides an adaptive mechanism for many restoration algorithms instead of using fixed values for the setting of noise levels. This paper proposes a new superpixel-based framework associated with statistical analysis for estimating the variance of additive Gaussian noise in digital images. The proposed approach consists of three major phases: superpixel classification, local variance computation, and statistical determination. The normalized cut algorithm is first adopted to effectively divide the image into a set of superpixel regions, from which the noise variance is computed and estimated. Subsequently, the Jarque–Bera test is used to exclude regions that are not normally distributed. The smallest standard deviation in the remaining regions is finally selected as the estimation result. A wide variety of noisy images with various scenarios were used to evaluate this new noise estimation algorithm. Experimental results indicated that the proposed framework provides accurate estimations across various noise levels. Comparing with many state-of-the-art methods, our algorithm strikes a good compromise between low-level and high-level noise estimations. It is suggested that the proposed method is of potential in many computer vision, image, and video processing applications that require automation.

Noise is one of the main sources of quality deterioration not only for visual inspection but also in computerized processing in brain magnetic resonance (MR) image analysis such as tissue classification, segmentation and registration. Accordingly, noise removal in brain MR images is important for a wide variety of subsequent processing applications. However, most existing denoising algorithms require laborious tuning of parameters that are often sensitive to specific image features and textures. Automation of these parameters through artificial intelligence techniques will be highly beneficial. In the present study, an artificial neural network associated with image texture feature analysis is proposed to establish a predictable parameter model and automate the denoising procedure. In the proposed approach, a total of 83 image attributes were extracted based on four categories: 1) Basic image statistics. 2) Gray-level co-occurrence matrix (GLCM). 3) Gray-level run-length matrix (GLRLM) and 4) Tamura texture features. To obtain the ranking of discrimination in these texture features, a paired-samples t-test was applied to each individual image feature computed in every image. Subsequently, the sequential forward selection (SFS) method was used to select the best texture features according to the ranking of discrimination. The selected optimal features were further incorporated into a back propagation neural network to establish a predictable parameter model. A wide variety of MR images with various scenarios were adopted to evaluate the performance of the proposed framework. Experimental results indicated that this new automation system accurately predicted the bilateral filtering parameters and effectively removed the noise in a number of MR images. Comparing to the manually tuned filtering process, our approach not only produced better denoised results but also saved significant processing time.