Physically Motivated Enhancement of Color Images for Fiber Endoscopy

Abstract

Fiber optics are widely used in flexible endoscopes which are indispensable for many applications in diagnosis and therapy. Computeraided use of fiberscopes requires a digital sensor mounted at the proximal end. Most commercially available cameras for endoscopy provide the images by means of a regular grid of color filters what is known as the Bayer Pattern. Hence, the images suffer from false colored spatial moiré, which is further stressed by the downgrading fiber optic transmission yielding a honey comb pattern. To solve this problem we propose a new approach that extends the interpolation between known intensities of registered fibers to multi channel color applications. The inventive idea takes into account both the Gaussian intensity distribution of each fiber and the physical color distribution of the Bayer pattern. Individual color factors for interpolation of each fiber area make it possible to simultaneously remove both the comb structure from the fiber bundle as well as the Bayer pattern mosaicking from the sensor while preserving depicted structures and textures in the scene.

Simple imaging protocol for autofluorescence elimination and optical sectioning in fluorescence endomicroscopy

“…To further minimize instrument complexity and costs, modulation of lights and triggering of the acquisition by the camera are synchronized by an Arduino-compatible card (Teensy 3.5). The series of acquired fluorescence images are processed on PC by applying a time-domain Fourier transform in order to yield the timeaveraged and out-of-phase images (subsequently denoted as Pre-OPIOM and Speed OPIOM, respectively), in which the honeycomb pattern from the bundle is removed by an interpolation algorithm [42] (see Section 1 and Fig. S1 in Supplement 1).…”

Image computing for fibre-bundle endomicroscopy: A review

“…(Zheng et al, 2017) attempted to refine the results of a bilinear interpolation using a rotationally invariant adaptation of Non-Local Means (NLM) filters. Moreover, (Winter et al, 2007) proposed an extension of the core interpolation approach for single chip colour cameras, suppressing any false colour moiré patterns. While higher order continuity generated smoother images, a property that can be desirable in particular applications, the associated reconstruction accuracy was shown (Rupp et al, 2009) to be only marginally superior to simple C 0 algorithms.…”

Surface roughness evaluation of additive manufactured metallic components from white light images captured using a flexible fiberscope

“…Interpolation technique for comb structure removal utilizes a two-part algorithm designed to exploit the intensity variations between two neighbouring fibers [37]. The first part of the interpolation algorithm localizes the fiber centres based on a calibration image captured by illuminating the distal end using a white light illumination.…”

“…The first part of the interpolation algorithm localizes the fiber centres based on a calibration image captured by illuminating the distal end using a white light illumination. The second part interpolates the intensity values between two adjacent fibers to form a uniform image [37]. Compared to the other two techniques, the interpolation method requires time-to-time recalibrations.…”