Byeongho Moon

Low-light image enhancement using variational optimization-based Retinex model

This paper presents an optimization-based low-light image enhancement method using spatially adaptive �������� -norm based Retinex model. The proposed method adaptively enforces the regularization parameter using the spatially adaptive weight map, which is generated using the bright channel prior (BCP) and local variance map. Since the proposed weight map assigns the smaller weight value at the bright and edge region, the proposed method can perform weak noise reduction to preserve the edges and textures. In addition, the simplified version of the proposed method is presented using the FFT and quantized weight values for the application to consumer devices. Experimental results show that the proposed method can provide better enhanced result without the ι2 -norm minimization artifacts at the low computational cost.

Continuous digital zooming of asymmetric dual camera images using registration and variational image restoration

This paper presents a theoretical basis to realize a high-quality digital zooming using two camera modules with different focal lengths. First, we describe an image degradation model of the asymmetric dual camera system to analyze the characteristic of the wide- and tele-view images. In an asymmetric dual camera system, we assume that the shorter focal length module produces the wide-view image with the low-resolution. On the other hand, the longer focal length module produces the tele-view image by an optical zooming. To reconstruct a wide-view image of a continuous digital zooming, the proposed method first estimates the point spread function (PSF) between the wide- and tele-view images. Next, the proposed method performs variational-based image restoration using the estimated PSF. In addition, since the tele-view image inserted into appropriate region of the wide-view image, the proposed method can provide significantly improved wide-view image.

Variational framework for low-light image enhancement using optimal transmission map and combined ℓ1 and ℓ2-minimization

Abstract This paper presents a novel variational framework for low-light image enhancement. The proposed enhancement algorithm simultaneously performs brightness enhancement and noise reduction using a variational optimization. An edge-preserved noise reduction is performed by minimizing the total variation constraint term in the energy function. In addition, the proposed method estimates the optimal transmission map to restore the low-light image by minimizing the l 2 -norm smoothness and data-fidelity terms. To minimize the proposed energy functional, the proposed method splits the l 1 -derivative term under the split Bregman iteration framework. The performance of the proposed method is evaluated using both simulated and natural low-light images. Experimental results show that the proposed enhancement method can significantly improve the quality of the low-light images without noise amplification.

Local self similarity-based super-resolution for asymmetric dual-camera

This paper presents a super-resolution (SR) method for the asymmetric dual-camera system. The proposed method restores the high-resolution (HR) image using the local self-similarity between two input images as wide- and tele-view images. The proposed method consists of three steps: registration and warping processes to reduce the disparity between the input images, high-frequency patch extraction from the tele-view image, and super-resolution reconstruction using local self-similarity property. Experimental results show that the proposed method can significantly restore the HR image than existing interpolation and SR methods.