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This paper presents a method for transferring haze information between the source and target images. First, we applied the dark channel prior method to the input source and target images. The dark channel prior method was proposed for removing haze from an image. By applying the method thereof, an input image can be decomposed to a scene radiance image, global atmospheric color (haze color) vector, and transmission map. Subsequently, for transferring the haze information, we applied the color transfer method to the decomposed information of source and target images. Finally, we reconstructed a haze-transferred image from the decomposed images. Our results indicate that the proposed haze transfer method can reproduce fine haze-transferred appearance, compared with the input source and target images.

Projection mapping can control object surface appearances without painting or finishing. An existing problem in projection mapping is the control of skin appearances. This paper presents a spectral projection mapping method to reproduce makeup skin appearances. Spectral information is significant for the physically accurate reproduction of human skin. First, we propose a spectral projection mapping model for computing the projection spectra and controlling skin appearance. Subsequently, we developed a spectral projection mapping system for realizing skin appearance control. For projecting spatially varying spectra, we used spectral basis functions by non-negative matrix factorization. In our experiment, actual makeup skins and projection-mapped skins were compared spectrally. The results indicated that our method could reproduce makeup skins with good accuracy.

This paper presents a novel video smoke removal method based on a smoke imaging model and space-time pixel compensation. First, we develop an optical imaging model for natural scenes that contain smoke. Then, we remove the smoke in a video, frame-by-frame, based on the smoke imaging model and conventional dehazing approaches. Next, we align the smoke-removed frames using corresponding pixels. To obtain the corresponding pixels, we use SIFT and color features with distance constraints. Finally, to reproduce clear video appearance, we compensate pixel values by utilizing the space-time weightings of the corresponding pixels between the smoke-removed frames. Validation experiments show our method can provide effective smoke removal resulting in dynamic scenes.

This paper presents a method to measure spectral reflectance and 3D shape of an object. For realizing these measurements, we applied a multi-primary image projector as a computational illumination system. This multi-primary image projector employs a light source which is programmable and can reproduce any spectral power distributions. In other words, the projector can reproduce 2D pattern projections with arbitrary spectra. In our actual measurements, we developed an imaging system by synchronizing the multi-primary image projector and a highspeed monochrome camera. First, the surface spectral reflectance of an object in a darkroom was obtained based on a finite-dimensional linear model of spectral reflectances. In the spectral reflectance measurements, nine basis images were projected and captured by the synchronized imaging system. Then spectral reflectance at each camera image coordinate was estimated from the captured nine images. Next, structured lights were projected for reconstructing 3D shape. We applied eight binary image projections and a conventional 3D shape reconstruction algorithm to our study. In summary, seventeen images were projected and captured for measuring spectral reflectance and 3D shape. The projection and capturing speed of the seventeen images is 0.085 s on the system specification. In the validation experiments, we could obtain spectral reflectance of X-rite ColorChecker with the average color difference \(\varDelta E_{ab}^{*}\) of approximately 4. We also confirmed that precise 3D shapes could be reconstructed by our method.

In this paper, we propose a method to improve the visual quality of color-embedded gray images for a reversible color-to-gray algorithm. The reversible color-to-gray algorithm is an effective technique that considers several perspectives such as running cost, data quantity, and security in practical applications. Many reversible color-to-gray algorithms have been proposed, but most of these studies have only evaluated already-recovered color images. Although the visual quality of color-embedded gray images is also important, there has not been much assessment in this area. In this paper, we present a Fourier-based reversible color-to-gray algorithm to realize an improved visual quality of color-embedded gray images while maintaining the quality of the recovered color image. We also construct a color image recovery system from a printed gray image captured by a monochrome camera. Experimental results show that the proposed algorithm can improve the visual quality of both the color-embedded gray image and the recovered image in the closed-computer world, but the algorithm does not perform well for a printed gray images.

Relighting is a technique to modify an image to account for alternate illumination conditions. Conventionally, the reflectance characteristics of an object are provided, and a relighted image is calculated using input light source information. In this paper, we propose a dynamic relighting system for moving planar objects with unknown reflectance. By acquiring the surface spectral reflectance of moving objects, our system is able to reproduce accurate colors on a display device. In the reflectance acquisition, we use a programmable light source that can produce any spectral curve. The surface spectral reflectance of an object in a darkroom is obtained based on the lighting technique with five spectral basis functions that are generated by the programmable light source. The acquired reflectance and user-input illumination information are used to calculate accurate CIEXYZ values of the relighted image. Finally, CIEXYZ values are accurately transformed to RGB values. In the experiment, illuminant A and D65 are used as the illuminants for relighting. As a result, by comparing a computer simulation with actual experiments with real objects, we observe an average color difference \( \Delta {\text{E}}^{*}_{\text{ab}} \) of approximately 7. This system operates at a rate of 1 frame per second. In addition, in this study, we have implemented another relighting system for objects under an environmental lighting condition by determining the spectral power distribution of the illumination source.