The lighting up of buildings is one form of entertainment that makes a city more colorful, and photographers sometimes change this lighting using photo-editing applications. This paper proposes a method for automatically performing such changes that follows the Retinex theory. Retinex theory indicates that the complex scenes caught by the human visual system are affected by surrounding colors, and Retinex-based image processing uses these characteristics to generate images. Our proposed method follows this approach. First, we propose a method for extracting a relighting saliency map using Retinex with edge-preserving filtering. Second, we propose a sampling method to specify the lighting area. Finally, we composite the additional light to match the human visual perception. Experimental results show that the proposed sampling method is successful in keeping the illuminated points in bright locations and equally spaced apart. In addition, the proposed various diffusion methods can enhance nighttime skyline photographs with various expressions. Finally, we can add in a new light by considering Retinex theory to represent the perceptual color.

Finding the optimal implementation of calculations is one of the most critical challenges in image processing programming. Halide is a domain-specific language for high-performance image processing. Its auto-scheduler is a helpful tool for solving this problem; however, its scheduling is not a panacea for complex flows. In this paper, we evaluate the performance of the auto-scheduler by comparing it to hand-manually implemented C++ codes. The algorithm used for comparison is Directional cubic convolution interpolation (DCCI), whose computation schedule is challenging to optimize. We evaluate three auto-schedulers: Adams et al. 's, Li et al.'s, and Mullapudi et al.s. Experimental results show that the performance of the schedule generated by Adams' method is comparable to that of the hand-implemented C++ code.

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In this paper, we propose to speed up bilateral filtering by principal component analysis (PCA)-based dimensionality compression method with constant-time bilateral filtering. Constant-time bilateral filtering speeds up the filtering by representing it as a summation of the multiple Gaussian filters. However, a simple implementation is of the order of O(K 3 ) for color and suffers from the curse of dimensionality. A clustering-based approximation speedup solves this problem with an order of O(K) or O(K 2 ). PCA can provide a more informative signal relative to the transformation matrix. We have accelerated the process by converting the color information of the input image from 3-channel to 1-channel by PCA, considering the constant-time bilateral filter as joint bilateral filtering, and using the transformed image as a guide image. This dimensional reduction allowed us to filter images with sufficient accuracy at a higher speed.

The lighting up of buildings is one of the entertainments that make a city more colorful, and photographer sometimes changes the lighting by using photo-editing applications. This paper proposes a method for automatically performing such an application that follows the Retinex theory. The Retinex theory indicates that the complex scenes caught by the human visual system are decomposed into illumination and reflectance components. Many Retinex-based applications take much cost into the decomposition, while we show that the decomposition accuracy is less dependent on the final output for the nightscape relighting. First, we propose an extracting method of a relighting saliency map using Retinex with edge-preserving filtering. Second, we propose a sampling method to specify the lighting area in the limited area. We show that relighting within the limited area can be represented without illumination and reflectance decomposition. We then show that this fact can be exploited to realize this application using a simple Retinex decomposition of the single-scale Retinex with fast edge-preserving filters to represent various lighting effects. Experimental results show that the proposed sampling method is successful in keeping the illuminated points in bright locations and equally spaced.Also, the proposed various diffusion method can enhance night skyline photographs with various expressions. Our code and dataset are available at https://github.com/norishigefukushima/RelightingUpNightPhotography.