This paper presents an image deblurring method using 0-norm based deblurring and 2-norm based textureaware image fusion for remote sensing images. To restore the details of blurred texture, the proposed method first perform texture restoration by fusing the restored results using Richardson-Lucy deconvolution and unsharp masking. Next, we analyzed the intensity and dark channel properties of remote sensing images and perform the 0-norm based deblurring using the intensity and dark channel priors. Although the 0-norm based deblurring can provide a significantly restored result, it cannot overcome the loss of the texture region. On the other hand, the proposed 2norm based image fusion method can preserve both sharp edges and texture details. In the experiments, we demonstrate that the proposed method can provide better restored results than existing state-of-the-art deblurring methods without over-smoothing and undesired artifact.
In this paper, we present a novel low-light image enhancement method by combining optimization-based decomposition and enhancement network for simultaneously enhancing brightness and contrast. The proposed method works in two steps including Retinex decomposition and illumination enhancement, and can be trained in an end-to-end manner. The first step separates the low-light image into illumination and reflectance components based on the Retinex model. Specifically, it performs model-based optimization followed by learning for edge-preserved illumination smoothing and detail-preserved reflectance denoising. In the second step, the illumination output from the first step, together with its gamma corrected and histogram equalized versions, serves as input to illumination enhancement network (IEN) including residual squeeze and excitation blocks (RSEBs). Extensive experiments prove that our method shows better performance compared with state-of-the-art low-light enhancement methods in the sense of both objective and subjective measures.
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